Abstract

We present a stochastic simulation technique for subset selection in time series models, based on the use of indicator variables with the Gibbs sampler within a hierarchical Bayesian framework. As an example, the method is applied to the selection of subset linear AR models, in which only significant lags are included. Joint sampling of the indicators and parameters is found to speed convergence. We discuss the possibility of model mixing where the model is not well determined by the data, and the extension of the approach to include non-linear model terms. 1. INTRODUCTION Until recently, research into time series modelling has concentrated on those models which are analytically convenient, without necessarily justifying underlying assumptions such as linearity. With rapidly increasing computing power, it is now possible to consider a much wider range of models, including hybrids containing terms from several non-linear model families. The problem becomes one of subset selection --- ...

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